Apply clang-format on repository

Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.

Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/

There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.

Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>

1 files changed, 35 insertions, 20 deletions

diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp
index 9f96479295..d37cf4a8d0 100644
--- a/src/runtime/NEON/functions/NEReduceMean.cpp
+++ b/src/runtime/NEON/functions/NEReduceMean.cpp
@@ -25,21 +25,24 @@
 
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
 #include "src/common/utils/Log.h"
 #include "src/core/CPP/Validate.h"
-#include "src/core/NEON/kernels/NEReductionOperationKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/NEON/kernels/NEReductionOperationKernel.h"
 
 namespace arm_compute
 {
 namespace
 {
-Status validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
+Status
+validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
 {
     ARM_COMPUTE_UNUSED(keep_dims);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8,
+                                                         DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1);
     ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
 
@@ -47,29 +50,29 @@ Status validate_config(const ITensorInfo *input, const Coordinates &reduction_ax
     const int          input_dims    = input->num_dimensions();
     Coordinates        axis_local    = reduction_axis;
 
-    for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i)
+    for (unsigned int i = 0; i < axis_local.num_dimensions(); ++i)
     {
         //axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)).
         ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast<int>(input->num_dimensions())));
         ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast<int>(input->num_dimensions()));
     }
 
-    if(output->tensor_shape().total_size() != 0)
+    if (output->tensor_shape().total_size() != 0)
     {
         // Only validate if not using auto_init for the output tensor
         TensorShape out_shape = input->tensor_shape();
         // Validate output_shape only if not using auto_init
         convert_negative_axis(axis_local, input_dims);
         std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
-        for(unsigned int i = 0; i < reduction_ops; ++i)
+        for (unsigned int i = 0; i < reduction_ops; ++i)
         {
             ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
             ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1);
-            if(output->total_size() > 0 && keep_dims)
+            if (output->total_size() > 0 && keep_dims)
             {
                 ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
             }
-            if(keep_dims)
+            if (keep_dims)
             {
                 out_shape.set(axis_local[i], 1);
             }
@@ -91,11 +94,19 @@ Status validate_config(const ITensorInfo *input, const Coordinates &reduction_ax
 NEReduceMean::~NEReduceMean() = default;
 
 NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims()
+    : _memory_group(std::move(memory_manager)),
+      _reduction_kernels(),
+      _reduced_outs(),
+      _reshape(),
+      _reduction_ops(),
+      _keep_dims()
 {
 }
 
-Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
+Status NEReduceMean::validate(const ITensorInfo *input,
+                              const Coordinates &reduction_axis,
+                              bool               keep_dims,
+                              const ITensorInfo *output)
 {
     return validate_config(input, reduction_axis, keep_dims, output);
 }
@@ -107,7 +118,8 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis,
     // Perform validate step
     ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info()));
     // Output auto inizialitation if not yet initialized
-    const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input->info(), reduction_axis, keep_dims);
+    const TensorShape output_shape =
+        arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input->info(), reduction_axis, keep_dims);
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
 
     _reduction_ops = reduction_axis.num_dimensions();
@@ -124,37 +136,40 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis,
     convert_negative_axis(axis_local, input_dims);
 
     // Perform reduction for every axis
-    for(int i = 0; i < _reduction_ops; ++i)
+    for (int i = 0; i < _reduction_ops; ++i)
     {
-        TensorShape out_shape = i == 0 ? tmp_input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
+        TensorShape out_shape =
+            i == 0 ? tmp_input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
         out_shape.set(axis_local[i], 1);
         auto in = (i == 0) ? tmp_input : (&_reduced_outs[i - 1]);
 
-        if(i == _reduction_ops - 1 && keep_dims)
+        if (i == _reduction_ops - 1 && keep_dims)
         {
             _reduction_kernels[i].configure(in, tmp_output, axis_local[i], ReductionOperation::MEAN_SUM);
         }
         else
         {
-            _reduced_outs[i].allocator()->init(TensorInfo(out_shape, tmp_output->info()->num_channels(), tmp_output->info()->data_type(), tmp_output->info()->quantization_info()));
+            _reduced_outs[i].allocator()->init(TensorInfo(out_shape, tmp_output->info()->num_channels(),
+                                                          tmp_output->info()->data_type(),
+                                                          tmp_output->info()->quantization_info()));
             _memory_group.manage(&_reduced_outs[i]);
             _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], ReductionOperation::MEAN_SUM);
         }
     }
 
     // Allocate intermediate tensors
-    for(int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
+    for (int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
     {
         _reduced_outs[i].allocator()->allocate();
     }
     // Configure reshape layer if we want to drop the dimensions
-    if(!keep_dims)
+    if (!keep_dims)
     {
         TensorShape out_shape = tmp_input->info()->tensor_shape();
         // We have to sort the reduction axis vectors in order for remove_dimension
         // to work properly
         std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
-        for(int i = 0; i < _reduction_ops; ++i)
+        for (int i = 0; i < _reduction_ops; ++i)
         {
             out_shape.remove_dimension(axis_local[i] - i, false);
         }
@@ -166,11 +181,11 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis,
 void NEReduceMean::run()
 {
     MemoryGroupResourceScope scope_mg(_memory_group);
-    for(auto &kernel : _reduction_kernels)
+    for (auto &kernel : _reduction_kernels)
     {
         kernel.run();
     }
-    if(!_keep_dims)
+    if (!_keep_dims)
     {
         _reshape.run();
     }